Source-free unsupervised cross-condition capacity prediction of lithium-ion batteries based on adversarial differences and temporal consistency

Key Points

• Capacity prediction shows high accuracy across varying conditions, improving battery management.
• Key metric indicates over 90% accuracy in unsupervised predictions under adversarial differences.
• Analysis employs a novel approach based on adversarial differences and temporal consistency.
• Highlights potential for enhancing performance monitoring in lithium-ion batteries without extensive data requirements.